Search for "Gemini 2.5"

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  • MAY 20, 2025 / Android

    What you should know from the Google I/O 2025 Developer keynote

    Top announcements from Google I/O 2025 focus on building across Google platforms and innovating with AI models from Google DeepMind, with key focus on new tools, APIs, and features designed to enhance developer productivity and create AI-powered experiences using Gemini, Android, Firebase, and web.

    What you should know from the Google I/O 2025 Developer keynote
  • MAY 23, 2025 / Gemini

    Gemini API I/O updates

    Announcing new features and models for the Gemini API, with the introduction of Gemini 2.5 Flash Preview with improved reasoning and efficiency, Gemini 2.5 Pro and Flash text-to-speech supporting multiple languages and speakers, and Gemini 2.5 Flash native audio dialog for conversational AI.

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  • MAY 21, 2025 / Google AI Studio

    An upgraded dev experience in Google AI Studio

    Google AI Studio has been upgraded to enhance the developer experience, featuring native code generation with Gemini 2.5 Pro, agentic tools, and enhanced multimodal generation capabilities, plus new features like the Build tab, Live API, and improved tools for building sophisticated AI applications.

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  • APRIL 16, 2025 / Gemini

    Making it easier to build with the Gemini API in Google AI Studio

    Google AI Studio now has an expanded gallery of starter apps, and other updates including more intuitive prompting experience, native code editing, and various interactive examples demonstrating Gemini model capabilities, plus a refreshed UI – so you can continue building with the Gemini API.

    Google AI Studio update included starter apps
  • MAY 20, 2025 / Gemini

    Building agents with Google Gemini and open source frameworks

    Google Gemini models offer several advantages when building AI agents, such as advanced reasoning, function calling, multimodality, and large context window capabilities. Open-source frameworks like LangGraph, CrewAI, LlamaIndex, and Composio can be used with Gemini for agent development.

    Building agents with Google Gemini and open source frameworks
  • JULY 10, 2025 / Cloud

    Advancing agentic AI development with Firebase Studio

    Updates in Firebase Studio include new Agent modes, foundational support for the Model Context Protocol (MCP), and Gemini CLI integration, all designed to redefine AI-assisted development allow developers to create full-stack applications from a single prompt and integrate powerful AI capabilities directly into their workflow.

    Advancing agentic AI development with Firebase Studio
  • APRIL 9, 2025 / AI

    Agent Development Kit: Making it easy to build multi-agent applications

    The Agent Development Kit (ADK), an open-source framework from Google designed to simplify the development of multi-agent systems, providing tools for building, interacting, evaluating, and deploying agents.

    Agent Development Kit: Making it easy to build multi-agent applications
  • MAY 20, 2025 / Gemini

    Fully Reimagined: AI-First Google Colab

    Google Colab is launching a reimagined AI-first version at Google I/O, featuring an agentic collaborator powered by Gemini 2.5 Flash with iterative querying capabilities, an upgraded Data Science Agent, effortless code transformation, and flexible interaction methods, aiming to significantly improve coding workflows.

    Google Colab's reimagined Al-first experience
  • MAY 9, 2025 / Cloud

    Google AI for game developers

    Revisit announcements from this year's Games Developer Conference (GDC). Explore how Gemma and Gemini models can aid in building AI experiences in games with the launch of Gemma 3, the Unity plugin, its application in a sample game, and scaling games with generative AI in Google Cloud.

    Google AI for Game Developers
  • JULY 7, 2025 / Gemini

    Batch Mode in the Gemini API: Process more for less

    The new batch mode in the Gemini API is designed for high-throughput, non-latency-critical AI workloads, simplifying large jobs by handling scheduling and processing, and making tasks like data analysis, bulk content creation, and model evaluation more cost-effective and scalable, so developers can process large volumes of data efficiently.

    Scale your AI workloads with batch mode in the Gemini API